def autoex(epochs=300,
nbits=4,
lrate=0.03,
showint=100,
mbs=None,
vfrac=0.1,
tfrac=0.1,
vint=100,
sm=False,
bestk=None):
size = 2**nbits
mbs = mbs if mbs else size
case_generator = (lambda: TFT.gen_all_one_hot_cases(2**nbits))
cman = Caseman(cfunc=case_generator, vfrac=vfrac, tfrac=tfrac)
ann = Gann(dims=[size, nbits, size],
cman=cman,
lrate=lrate,
showint=showint,
mbs=mbs,
vint=vint,
softmax=sm)
#ann.gen_probe(0,'wgt',('hist','avg')) # Plot a histogram and avg of the incoming weights to module 0.
#ann.gen_probe(1,'out',('avg','max')) # Plot average and max value of module 1's output vector
#ann.add_grabvar(0,'wgt') # Add a grabvar (to be displayed in its own matplotlib window).
ann.run(epochs, bestk=bestk)
ann.runmore(epochs * 2, bestk=bestk)
return ann
def manage_data_loaders(function_name, params, loss_function):
one_hot = False
if (function_name == "load_mnist"):
cases = mnist(params, loss_function)
elif (function_name == "dataset_loader"):
cases = dataset_loader(params[0], loss_function)
elif (function_name == "gen_all_parity_cases"):
if (len(params) == 1):
cases = TFT.gen_all_parity_cases(int(params[0]))
else:
cases = TFT.gen_all_parity_cases(int(params[0]), bool(params[1]))
elif (function_name == "gen_all_one_hot_cases"):
one_hot = True
cases = TFT.gen_all_one_hot_cases(2**int(params[0]))
elif (function_name == "gen_vector_count_cases"):
one_hot = True
nof_cases = int(params[0])
length = int(params[1])
cases = TFT.gen_vector_count_cases(nof_cases, length)
elif (function_name == "gen_segmented_vector_cases"):
one_hot = True
# ASSUMING all these parameters are given:
try:
nbits = int(params[0])
nof_cases = int(params[1])
min_seg = int(params[2])
max_seg = int(params[3])
except:
print(
"Not enough arguments given! [nbits, nof_cases, min_seg, max_seg]"
)
return
cases = TFT.gen_segmented_vector_cases(nbits, nof_cases, min_seg,
max_seg)
# Sparse need integers as labels, not vectors:
if (loss_function == "sparse_softmax_cross_entropy"):
bit_cases = []
for case in cases:
if (function_name == "gen_all_parity_cases"):
if (len(case[1]) == 1):
bit_cases.append([case[0], case[1]])
else:
bit_cases.append([case[0], case[1][1]])
else:
for i, b in enumerate(case[1]):
if (b):
bit_cases.append([case[0], i])
break
def autoencoder(self):
nbits = int(input("Enter the length of the vector in bits. "
"Please be careful and not crash my shit with a number like 32: "))
case_generator = (lambda: TFT.gen_all_one_hot_cases(2 ** nbits))
case_man = Caseman(cfunc=case_generator, vfrac=self.params.vfrac, tfrac=self.params.tfrac)
self.ann.set_cman(case_man)
self.params.dims[0] = len(case_man.training_cases[0][0])
self.params.dims[-1] = len(case_man.training_cases[0][1])
print("\nNumber of bits taken from input layer: ", self.params.dims[0],
"and output set to target vector length at: ", self.params.dims[-1])
model = self.build_ann()
self.ann.set_model(model)
# model.gen_probe(0, 'wgt', ('hist', 'avg')) # Plot a histogram and avg of the incoming weights to module 0.
# model.gen_probe(1, 'out', ('avg', 'max')) # Plot average and max value of module 1's output vector
# model.add_grabvar(0, 'wgt') # Add a grabvar (to be displayed in its own matplotlib window).
model.run(steps=self.params.steps, bestk=self.params.bestk)
self.check_mapping_and_dendro()
def autoex(epochs=10000,
nbits=4,
lrate=0.03,
showint=100,
mbs=None,
vfrac=0.1,
tfrac=0.1,
vint=100,
sm=False,
bestk=None):
size = 2**nbits
mbs = mbs if mbs else size
case_generator = (lambda: TFT.gen_all_one_hot_cases(2**nbits))
cman = Caseman(cfunc=case_generator, vfrac=vfrac, tfrac=tfrac)
ann = Gann(dims=[size, nbits, size],
cman=cman,
lrate=lrate,
showint=showint,
mbs=mbs,
vint=vint,
softmax=sm)
#ann.gen_probe(0,'wgt',('hist','avg')) # Plot a histogram and avg of the incoming weights to module 0.
#ann.gen_probe(1,'out',('avg','max')) # Plot average and max value of module 1's output vector
#ann.add_grabvar(0,'wgt') # Add a grabvar (to be displayed in its own matplotlib window).
ann.run(epochs, bestk=bestk)
ann.runmore(epochs * 2, bestk=bestk)
correct = tf.equal(tf.argmax(ann.target, 1), tf.argmax(ann.output, 1))
accuracy = tf.reduce_mean(tf.cast(correct, "float"))
print("Accuracy")
ann.reopen_current_session()
inputs = [c[0] for c in cman.get_training_cases()]
targets = [c[1] for c in cman.get_training_cases()]
print(
ann.current_session.run(accuracy,
feed_dict={
ann.input: inputs,
ann.target: targets
}))
return ann
def autoex(epochs=300,
nbits=4,
learning_rate=0.03,
showint=100,
batch_size=None,
vfrac=0.1,
tfrac=0.1,
vint=100,
sm=False,
bestk=None):
size = 2**nbits
batch_size = batch_size if batch_size else size
case_generator = (lambda: TFT.gen_all_one_hot_cases(2**nbits))
case_manager = CaseManager(cfunc=case_generator, vfrac=vfrac, tfrac=tfrac)
ann = Gann(dims=[size, nbits, size],
case_manager=case_manager,
learning_rate=learning_rate,
showint=showint,
batch_size=batch_size,
vint=vint,
softmax=sm)
ann.run(epochs, bestk=bestk)
ann.runmore(epochs * 2, bestk=bestk)
return ann
def create_gann(self, name, networkDimsString, hiddenActivationFunc,
outputActivationFunc, lossFunc, optimizer, optimizerParams,
learningRate, weightInitType, weightInit, dataSource,
dSourcePars, caseFrac, valFrac, testFrac, miniBatchSize):
# Convert strings of numbers to list of ints
if weightInitType == "uniform":
weightInit = [float(i) for i in weightInit.split(" ")]
else:
weightInit = (0, 0)
networkDims = [int(i) for i in networkDimsString.split(" ")]
if dSourcePars == '': dSourcePars = []
else: dSourcePars = [int(i) for i in dSourcePars.split(" ")]
if optimizerParams == '': optimizerParams = []
else: optimizerParams = [float(i) for i in optimizerParams.split(" ")]
# Find the correct case-generating function
case_generator = None
if (dataSource == 'bitcounter'):
case_generator = (lambda: TFT.gen_vector_count_cases(
dSourcePars[0], dSourcePars[1]))
elif (dataSource == 'autoencoder'):
case_generator = (
lambda: TFT.gen_all_one_hot_cases(dSourcePars[0]))
elif (dataSource == 'dense_autoencoder'):
case_generator = (lambda: TFT.gen_dense_autoencoder_cases(
count=dSourcePars[0],
size=dSourcePars[1],
dr=(dSourcePars[2], dSourcePars[3])))
elif (dataSource == 'parity'):
case_generator = (lambda: TFT.gen_all_parity_cases(dSourcePars[0]))
elif (dataSource == 'segment'):
case_generator = (lambda: TFT.gen_segmented_vector_cases(
vectorlen=dSourcePars[0],
count=dSourcePars[1],
minsegs=dSourcePars[2],
maxsegs=dSourcePars[3]))
elif (dataSource == 'MNIST'):
case_generator = (lambda: TFT.gen_mnist_cases())
elif (dataSource == 'wine'):
case_generator = (
lambda: TFT.gen_uc_irvine_cases('winequality_red'))
elif (dataSource == 'glass'):
case_generator = (lambda: TFT.gen_uc_irvine_cases('glass'))
elif (dataSource == 'yeast'):
case_generator = (lambda: TFT.gen_uc_irvine_cases('yeast'))
elif (dataSource == 'hackers'):
case_generator = (
lambda: TFT.gen_hackers_choice_cases('balance-scale'))
else:
raise NotImplementedError("Datasource: " + dataSource +
" is not implemented")
cMan = Caseman(cfunc=case_generator,
cfrac=float(caseFrac),
vfrac=float(valFrac),
tfrac=float(testFrac))
ann = Gann(name=name,
netDims=networkDims,
cMan=cMan,
learningRate=float(learningRate),
mbs=int(miniBatchSize),
hiddenActivationFunc=hiddenActivationFunc,
outputActivationFunc=outputActivationFunc,
lossFunc=lossFunc,
optimizer=optimizer,
optimizerParams=optimizerParams,
weightInitType=weightInitType,
weightRange=weightInit)
self.ann = ann
def __init__(self, nh=3, lr=.1):
self.cases = TFT.gen_all_one_hot_cases(2**nh)
self.learning_rate = lr
self.num_hiddens = nh
self.build_neural_network(nh)
def source(self):
def normalize(cases):
input = [c[0] for c in cases]
target = [c[1] for c in cases]
input = numpy.array(input)
min_arr = numpy.min(input, axis=0)
max_arr = numpy.max(input, axis=0)
for element in input:
for i, e in enumerate(element):
element[i] = (e - min_arr[i]) / (max_arr[i] - min_arr[i])
return list(zip(input, target))
def to_float(inp):
# returns 0 if input is ? (questionmark)
return 0 if inp == '?' else float(inp)
self.source_is_called = True
print("source:", self.args.source)
data_set = []
if self.args.source[-4:] == ".txt":
with open("data_set_files/" + self.args.source) as file:
data = list(
map(lambda x: re.split("[;,]", x), file.readlines()))
data = list(map(lambda x: list(map(to_float, x)), data))
max_d = max(map(lambda x: int(x[-1]), data))
for element in data:
input = element[:-1]
target = TFT.int_to_one_hot(int(element[-1]) - 1, max_d)
data_set.append([input, target])
elif self.args.source == "parity":
if self.args.sourceinit is None:
data_set = TFT.gen_all_parity_cases(10)
else:
data_set = TFT.gen_all_parity_cases(self.args.sourceinit[0])
elif self.args.source == "symmetry":
if self.args.sourceinit is None:
vecs = TFT.gen_symvect_dataset(101, 2000)
else:
vecs = TFT.gen_symvect_dataset(self.args.sourceinit[0],
self.args.sourceinit[1])
inputs = list(map(lambda x: x[:-1], vecs))
targets = list(map(lambda x: TFT.int_to_one_hot(x[-1], 2), vecs))
data_set = list(zip(inputs, targets))
elif self.args.source == "auto_onehot":
if self.args.sourceinit is None:
data_set = TFT.gen_all_one_hot_cases(64)
else:
data_set = TFT.gen_all_one_hot_cases(self.args.sourceinit[0])
elif self.args.source == "auto_dense":
if self.args.sourceinit is None:
data_set = TFT.gen_dense_autoencoder_cases(2000, 100)
else:
data_set = TFT.gen_dense_autoencoder_cases(
self.args.sourceinit[0], self.args.sourceinit[1])
elif self.args.source == "bitcounter":
if self.args.sourceinit is None:
data_set = TFT.gen_vector_count_cases(500, 15)
else:
data_set = TFT.gen_vector_count_cases(self.args.sourceinit[0],
self.args.sourceinit[1])
elif self.args.source == "segmentcounter":
if self.args.sourceinit is None:
data_set = TFT.gen_segmented_vector_cases(25, 1000, 0, 8)
else:
data_set = TFT.gen_segmented_vector_cases(self.args.sourceinit[0], \
self.args.sourceinit[1], self.args.sourceinit[2], self.args.sourceinit[3])
elif self.args.source == "mnist":
# mnist_basics.load_all_flat_cases(type='testing')
cases = mnist_basics.load_all_flat_cases(type='training')
input = cases[0]
target = cases[1]
input = list(map(lambda x: list(map(lambda e: e / 255, x)), input))
target = list(map(lambda x: TFT.int_to_one_hot(x, 10), target))
data_set = list(zip(input, target))
if data_set == []:
print(self.args.source, " is illegal for argument --source")
print("Legal values are: <filenme>.txt, parity, symmetry, \
auto_onehot, auto_dense, bitcounter, segmentcounter",
sep="")
quit()
if self.args.source[-4:] == ".txt":
data_set = normalize(data_set)
return data_set
def data_loader(self, dataset, caseFraction, testFraction,
validationFraction):
if dataset == "parity":
length = int(input("Length of vectors: "))
doubleFlag = input("Activate double flag y/n: ")
ds = CaseManager(TFT.gen_all_parity_cases(length,
doubleFlag == "y"),
validation_fraction=validationFraction,
test_fraction=testFraction)
self.openAA.set_case_manager(ds)
#Default values for parity
self.mp.layer_dims = [10, 20, 40, 20, 1]
self.mp.learning_rate = 0.001
self.mp.hidden_activation_function = "relu"
self.mp.softmax = False
self.mp.w_range = "scaled"
self.mp.bestk = None
self.mp.epochs = 400
self.mp.error_function = "mse"
self.mp.optimizer = "adam"
self.mp.minibatch_size = 100
self.mp.lr_freq = None
self.mp.bs_freq = None
self.custom_buckets = None
self.target_accuracy = None
self.early_stopping = False
#use this to set size of input layer
print("Input size: " + str(len(ds.training_cases[0][0])) +
", Output size: " + str(len(ds.training_cases[0][1])))
elif dataset == "symmetry":
vectorNumber = int(input("Number of cases: "))
vectorLength = int(input("Length of vectors: "))
ds = CaseManager(TFT.gen_symvect_dataset(vectorLength,
vectorNumber),
validation_fraction=validationFraction,
test_fraction=testFraction)
self.openAA.set_case_manager(ds)
#Default values for symmetry
self.mp.layer_dims = [vectorLength, 40, 20, 1]
self.mp.learning_rate = 0.001
self.mp.hidden_activation_function = "relu"
self.mp.softmax = False
self.mp.w_range = "scaled"
self.mp.bestk = None
self.mp.epochs = 70
self.mp.optimizer = "adam"
self.mp.error_function = "mse"
self.mp.minibatch_size = 8
self.mp.lr_freq = None
self.mp.bs_freq = None
self.custom_buckets = None
self.target_accuracy = None
self.early_stopping = False
print("Input size: " + str(len(ds.training_cases[0][0])) +
", Output size: " + str(len(ds.training_cases[0][1])))
elif dataset == "autoencoder":
vectorLength = int(input("Set lenght of vectors: "))
ds = CaseManager(TFT.gen_all_one_hot_cases(vectorLength),
validation_fraction=validationFraction,
test_fraction=testFraction)
self.openAA.set_case_manager(ds)
print("Input size: " + str(len(ds.training_cases[0][0])) +
", Output size: " + str(len(ds.training_cases[0][1])))
elif dataset == "bitcounter":
vectorNumber = int(input("Number of cases: "))
vectorLength = int(input("Length of input vector: "))
ds = CaseManager(TFT.gen_vector_count_cases(
vectorNumber, vectorLength),
validation_fraction=validationFraction,
test_fraction=testFraction)
self.openAA.set_case_manager(ds)
print("Input size: " + str(len(ds.training_cases[0][0])) +
", Output size: " + str(len(ds.training_cases[0][1])))
print(ds.training_cases[0])
#Default values for bitcounter
self.mp.layer_dims = [15, 128, 64, 32, 16]
self.mp.softmax = True
self.mp.hidden_activation_function = "relu"
self.mp.bestk = 1
self.mp.learning_rate = 0.001
self.mp.epochs = 100
self.mp.w_range = [-0.01, 0.1]
self.mp.error_function = "ce"
self.mp.optimizer = "adam"
self.mp.minibatch_size = 16
self.mp.lr_freq = None
self.mp.bs_freq = None
self.custom_buckets = None
self.target_accuracy = None
self.early_stopping = False
elif dataset == "segmentcounter":
vectorNumber = int(input("Number of cases: "))
vectorLength = int(input("Length of input vector: "))
minSeg = int(input("Minimum number of segments: "))
maxSeg = int(input("Maximum number of segments: "))
ds = CaseManager(TFT.gen_segmented_vector_cases(
vectorLength, vectorNumber, minSeg, maxSeg),
validation_fraction=validationFraction,
test_fraction=testFraction)
self.openAA.set_case_manager(ds)
print("Input size: " + str(len(ds.training_cases[0][0])) +
", Output size: " + str(len(ds.training_cases[0][1])))
self.mp.layer_dims = [25, 128, 64, 32, 12, 9]
self.mp.softmax = True
self.mp.hidden_activation_function = "relu"
self.mp.bestk = 1
self.mp.learning_rate = 0.0005
self.mp.epochs = 200
self.mp.w_range = [0, 0.1]
self.mp.error_function = "ce"
self.mp.optimizer = "adam"
self.mp.minibatch_size = 32
self.mp.lr_freq = None
self.mp.bs_freq = None
self.custom_buckets = None
self.target_accuracy = None
self.early_stopping = False
elif dataset == "mnist":
cases = mnist.load_flat_text_cases(
"all_flat_mnist_training_cases_text.txt")
if caseFraction != 1:
cases = TFT.get_fraction_of_cases(cases, caseFraction)
numbersFordeling = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
for case in cases:
num = case[1].index(1)
numbersFordeling[num] += 1
print("number of cases: " + str(len(cases)))
print(numbersFordeling)
ds = CaseManager(cases,
validation_fraction=validationFraction,
test_fraction=testFraction)
#Default values for mnist
self.mp.layer_dims = [784, 512, 10]
self.mp.softmax = False
self.mp.hidden_activation_function = "relu"
self.mp.bestk = 1
self.mp.learning_rate = 0.001
self.mp.w_range = [0, 0.1]
self.mp.epochs = 10
self.mp.error_function = "sce"
self.mp.optimizer = "adam"
self.mp.minibatch_size = 20
self.mp.lr_freq = None
self.mp.bs_freq = None
self.custom_buckets = None
self.target_accuracy = None
self.early_stopping = False
self.openAA.set_case_manager(ds)
print("Input size: " + str(len(ds.training_cases[0][0])) +
", Output size: " + str(len(ds.training_cases[0][1])))
print(ds.training_cases[0])
elif dataset == "wine":
print("\n")
#TODO : load wine dataset in correct format
filereader = fr.FileReader()
cases = filereader.readfile(
"wine.txt", 9 if self.mp.custom_buckets is None else 6,
[3, 4, 5, 6, 7, 8], True)
print("first: " + str(len(cases)))
if caseFraction != 1:
cases = TFT.get_fraction_of_cases(cases, caseFraction)
print("second: " + str(len(cases)))
ds = CaseManager(cases,
validation_fraction=validationFraction,
test_fraction=testFraction)
#Default values for wine
self.mp.layer_dims = [11, 512, 256, 128, 64, 32, 6]
self.softmax = False
self.hidden_activation_function = "relu"
self.mp.bestk = 1
self.mp.learning_rate = 0.002
self.mp.w_range = "scaled"
self.mp.epochs = 20
self.mp.error_function = "sce"
self.mp.optimizer = "adam"
self.mp.custom_buckets = [1]
self.mp.minibatch_size = 32
self.mp.lr_freq = 150
self.mp.bs_freq = 150
self.target_accuracy = None
self.early_stopping = False
self.openAA.set_case_manager(ds)
print((ds.training_cases[0]))
print((ds.training_cases[0][0]))
print((ds.training_cases[0][1]))
print("Input size: " + str(len(ds.training_cases[0][0])) +
", Output size: " + str(len(ds.training_cases[0][1])))
i = 0
for case in ds.training_cases:
try:
if len(case[0]) != len(ds.training_cases[0][0]):
print(len(case[0]))
except Exception as e:
print("HEI!! input")
print(case)
print("line nr " + str(i))
try:
if len(case[1]) != len(ds.training_cases[0][1]):
print(len(case[1]))
except Exception as e:
print("HEI!! target")
print(case)
print("line nr " + str(i))
i += 1
elif dataset == "glass":
print("\n")
filereader = fr.FileReader()
cases = filereader.readfile(
"glass.txt", 8 if self.mp.custom_buckets is None else 6,
[1, 2, 3, 5, 6, 7], True)
if caseFraction != 1:
cases = TFT.get_fraction_of_cases(cases, caseFraction)
ds = CaseManager(cases,
validation_fraction=validationFraction,
test_fraction=testFraction)
#Default values for glass
self.mp.layer_dims = [9, 512, 256, 64, 32, 6]
self.softmax = False
self.hidden_activation_function = "relu"
self.mp.bestk = 1
self.mp.learning_rate = 0.002
self.mp.w_range = [0, 0.1]
self.mp.epochs = 200
self.mp.error_function = "sce"
self.mp.optimizer = "adam"
self.mp.custom_buckets = [1]
self.mp.minibatch_size = 16
self.mp.lr_freq = 100
self.mp.bs_freq = 150
self.target_accuracy = None
self.early_stopping = False
self.openAA.set_case_manager(ds)
print((ds.training_cases[0]))
print((ds.training_cases[0][0]))
print((ds.training_cases[0][1]))
print("Input size: " + str(len(ds.training_cases[0][0])) +
", Output size: " + str(len(ds.training_cases[0][1])))
for case in ds.training_cases:
if len(case[0]) != len(ds.training_cases[0][0]):
print("HEI!! input")
if len(case[1]) != len(ds.training_cases[0][1]):
print("HEI!! target")
# ******* A General Artificial Neural Network ********
# This is the original GANN, which has been improved in the file gann.py
#Global variable including all the functions for generating different datasets. The name of the dataset is the key in the dict.
_generator = {
"symmetry":
lambda length, count: TFT.gen_symvect_dataset(
length, count
), #Needs spesific length and number of cases: length, count
"parity":
lambda a: TFT.gen_all_parity_cases(
a), #Need spesific length parameter, Double flag?
"autoencoder":
lambda a: (
TFT.gen_all_one_hot_cases(a)
), #or TFT.gen_dense_autoencoder_cases(count, size) #Needs a spesific length and size for the last a spesific density of one's
"bit counter":
lambda num, size: TFT.gen_vector_count_cases(
num, size), #Dimensions = [same as input, hidden, input + 1]
"segment counter":
lambda a, b, c, d: TFT.gen_segmented_vector_cases(a, b, c, d)
}
#Global variable for the hidden activation functions.
_hidden_activation_function = {
"sigmoid": lambda a, name: tf.nn.sigmoid(a, name),
"relu": lambda a, name: tf.nn.relu(a, name),
"tanh": lambda a, name: tf.nn.tanh(a, name)
}
'display_wgts': [],
'display_biases': [],
'dendrogram_layers': [],
'numeric': False
}
AUTOENCODER_CONFIG = {
'name': "autoencoder",
'steps': 30000,
'lrate': "scale",
'tint': 100,
'showint': 100,
'mbs': 100,
'wgt_range': (-.3, .3),
'hidden_layers': [4],
'hidac': (lambda x, y: tf.tanh(x, name=y)),
'outac': (lambda x, y: tf.nn.softmax(x, name=y)),
'case_generator': (lambda: TFT.gen_all_one_hot_cases(3)),
'stdeviation': False,
'vfrac': 0.0,
'tfrac': 0.0,
'cfunc': 'rmse',
'mapbs': 3,
'cfrac': 1.0,
'map_layers': [0, 1, 2],
'display_wgts': [1, 2],
'display_biases': [1, 2],
'dendrogram_layers': [1],
'numeric': False
}
